This section provides background information related to the present disclosure which is not necessarily prior art.
The allowable variation in physical anthropomorphic test dummies presents an issue with respect to building a predictive simulation model. In this regard, a finite element model for an anthropomorphic dummy will have a single response for a given input whereas different physical dummies of the same type will give a range of responses for the same input. The inherent dummy variability is a byproduct of the dummy response corridors and specifications, in part set by government regulators. Due to dummy variability, simulation models cannot be more predictive than the variation range of the physical dummy.
An example of how dummy variability can affect a test result can be seen with respect to a head acceleration test. The head acceleration allowed in the head drop certification test for a Hybrid III head is 225 g to 275 g equivalent to 250 g +/−10%. Head injury for frontal dummies is measured in HIC (Head Injury Criteria) and is a calculation based on head acceleration. The HIC calculation would then have an allowable variation for a dummy head of +/−27%.
While not all injury criteria are as sensitive to variation as HIC, the allowable dummy response corridors still have an effect. When considering the total dummy calibration variation as a whole, the range of dummy performance produced can create at least a one star variation in the proposed 2011 U.S. NCAP procedure.
Due to such variation, an OEM could perform a crash test in its own laboratory and achieve a particular U.S. NCAP star rating that could then vary by at least one star rating if the car is tested in another laboratory with a different dummy calibrated in a different lab. In order to better predict the results of a crash test, it is desirable to for a dummy simulation model to account for variation due to allowable response corridors.
For a simulation model to account for sources of variation, it is important to identify the sources of variation and identify the sensitivity of the variation on the product performance. Sources of variation include the following: mass variation for a given component; inertia variation for a given component due to mass variation and center of gravity location; material property variation with time; material property variation from batch to batch due to ambient conditions, procedural variations, temporal variations, environmental variations, and operator changes; material dimensional variation with time; material performance variation due to Mullins effect on rubber and vinyl; final part dimensions compared to CAD data; variable wall thickness of the molded parts (for vinyl only as urethane/silicone molds produced solid parts) around the periphery and along the length of a mold, especially true in areas with blend radii and could be due to different operators, different mould temperatures and different amounts of material entered into the mould—for vinyl the dwell process and how quickly the operator empties a mould after initial filling may affect this; voids in the foam; variable foam density throughout the foam volume; part variability due to operator and production process variation not mentioned above; assembly stress variation due to preloading during assembly i.e. jackets and zippers; dummy assembly set-up variation due to dummy joint settings; test setup variation during calibration, such as random errors, procedural variations, calibration uncertainties, operator variations, lab-to-lab variations, environmental factors; and instrumental variation, such as cross talk, sensor alignment, and sensor calibration.
The accuracy of sensor output may also be assessed to determine its effect on the test. For example, if a 10 kN full scale load cell has a non-linearity of 0.75%, it can have an error of 75N. If the load cell output is used in a test result with a 300N peak, the data could therefore be in error by 25%.
With respect to crosstalk of detected signals, it can have a 5% error on a channel. It is assessed during calibration as the effect on an unloaded channel when other channels are loaded and is typically a value proportional to the applied force on the loaded channel, i.e. lower forces result in lower absolute crosstalk values. Load cell accuracy may also have an effect on test output.
The total variation seen in dummy performance has significant contributions from the dummy hardware, the certification test method, and the instrumentation used. All three areas should be investigated if variation is to be accounted for.
If the dummy is improved and the variation in the physical product is reduced, then both the test method and instrumentation equipment used should both be able to show the improvement in the hardware product repeatability.
For the testing, this may involve a method of assessing the test performance independently of the hardware product. For example, a head and neck pendulum test would have a mechanical component assembly (mechanism) with integrated instrumentation, designed to produce and measure upper and lower neck loads and moments along with head accelerations. This mechanism quality would meet all A2LA requirements with respect to variability and repeatability. A similar device would be created for every certification piece of equipment.
Geometry: Models can be created using accurate external geometry of the dummy. Geometry data is collected in a variety of ways including surface scanning, CT Scanning, X-ray and use of CAD data that represents a designed part. Variations in material wall thickness for vinyl and Urethane parts are typically not considered with a nominal wall thickness used throughout a molded part. In many cases the Vinyl and foam parts are not represented by two different materials but as one material with a combined material characteristic.
Material Test Data: Dynamic material test data can be used by the simulation model and can only represent a single material test result from a range of test conditions. The material models used in the simulation represent a portion of the complete material model design space. No account is made for Mullins effect in the visco-elastic material properties or other material changes over time.
Component Mass: Many models use a single mass condition. Each part is modeled separately and the model mass is compared against the mass tolerance for that part. The model mass is considered acceptable if within the tolerance range.
Inertia Properties: Inertia properties in the finite element models are a by-product of the mass and geometry data used in the model. Inertia properties are compared to calculated properties via the CAD data. The inertia property of a physical model can vary based on material and geometric changes in the dummy.
Dummy Joints: Dummy joints in the FE models are represented using kinematic joints similar to a Madymo lumped mass joint. Joint properties can be estimated rather than determined through testing or against theoretical data. (Joint friction can be set with a “1 g” test. This is tightening the joint to the point it will not fall under its own weight. This test subject to operator technique and in some applications cannot be done for every dummy joint.)
Contact Interactions: Contact strategies for models can vary depending on the software used. A self-contact strategy can be used, but variables including penalty stiffness can create a significant variation in contact performance and model response.
Pre-Stress Parts: Dummy Jackets and other dummy components can create internal stresses within themselves and on other components when they are assembled into their final position. Typically, pre-stress conditions are not accounted for in the models, except to produce numerical stability. In addition, variation in the pre-stress condition caused by a variation in the component tolerances between dummies is not taken into account. For example, this can occur due to variations in the placements of the jacket zippers.
Mesh Density: Typically, mesh density is limited in a model to produce a solution time step of 10e−06. However, vehicle models are moving from 2-5 million to 5-10 million elements for the complete vehicle. Accuracy typically increases as mesh density increases. The dummy model can have a mesh density that is similar to the vehicle model. This helps reduce contact related modeling issues and tends to increase the accuracy of the models. Thus, the number of elements needed in a finite element dummy model may increase in line with the number of vehicle elements. Existing models can be updated accordingly.
Numerical Material Performance: Typical dummy models have a wide range of material performance. It is difficult for software companies to gain access to the correct samples for a given material. It is also difficult to then collect the material performance data under the correct load conditions. Rubber, vinyl, urethane, and foams are under a large deformation and have a high non-linear performance. In addition, these materials change their performance with a change in strain rate. It is desirable to collect material properties at the correct load conditions. It is also desirable to have test samples that represent dummy materials in their production condition. A number of new dummy drawing specifications will be needed for the materials of different components that are not currently specified in enough detail.
Instrumentation Content: There are various methods to model the instrumentation content of a dummy. For example, a model supplier may issue a given dummy model with a specific set of instrumentation included. The customer may use this model even though the dummy used in testing might have a different level of instrumentation included. Differences in instrumentation content within the model compared to the tested dummy can produce variation in the dummy total mass. Localized mass differences can then affect the dummy response. Current models do not account for these types of variations.
Generally, modeling variability includes the following steps: 1) determining the level of variation; 2) determining why the variation is present and the cause of the variation; 3) determining how easily the variation can be modeled; and 4) determining a process for modeling the variation.
Variation Level: Variation level can be determined through testing and generally includes material tests, component tests, and assembly system tests. For each test, a range of data at each level is generated that indicates the variation in performance. This can be done using a controlled experiment where potential sources of variation are identified and the testing is then completed such that the effect of the variation sources is demonstrated in the test results. Sources of variation in performance come from the dummy component and the test method used.
Reason For, and Cause Of, the Variation: At each stage of production and/or testing, the level of variation is identified and modeled. Variation can come from the dummy component and the manner in which the component is tested. An optimization method utilizing a genetic algorithm is used to find the optimal level of dummy and test variables. After a solution is determined, a design of experiment (DOE) is run around this condition using the defined variables. This identifies the effect of each variable on the variation.
As discussed above, there are two primary sources of variation, one from the dummy and one from the test. The optimization process uses input from both sources and is then able to identify the effect of the test and the dummy variation. The cause of the variation (by a variable) can be determined through the simulation. This is a natural output of the optimization process. Once the source of the variation is known, the reason for it occurring can be determined.
In addition to confirming the cause of the variation, the simulation process determines the magnitude of variation for a given source of variation. After the variation sources are identified and their effect is quantified, a repeat series of tests can be completed where the variation sources are minimized. The expected results are a reduction in the variability of the given performance. If this is not achieved, then steps 1 and 2 can be repeated until the desired level of improvement (reduction in variation and confirmation of the variation source) is achieved. This process improves the way the product is designed, manufactured, tested (repeatability) creating an increase in performance of the product (consistency).
From the work completed in steps 1 and 2 described above, the sources of variation will have been identified. The modeling process used identifies the variation source and its effect. Repeat testing helps determine if the simulation process produced the correct result. By default, the simulation shows that the variation can be modeled, but it is also desirable to know how easy it is to model the variation.
Material properties, mass, inertia, joint stiffness, cable tension, temperature effects, changes in test set up (initial positioning and impact energy) are relatively easy to model. Material thickness variation (vinyl thickness), anisotropic material properties, changes in surface shape, and volume are relatively difficult to model. The test and simulation process identifies the variation source and the magnitude of its effect. As such, it is then possible to identify which variables have the largest effect and how easy they are to model.
Process for Modeling the Variation: Material properties, temperature, mass and inertia variation will often have a dominant effect on dummy performance. During testing, it is likely that the initial positioning and impact energy variations will have the dominant effect on dummy performance. Model variations are normally made by introducing physical (manual) changes to the model input file. Using data from step 3, a modeling process can be developed to increase the ease in which a dominant variation source can be modeled. Once the variation source is known and its effect is understood, the most important variation modeling processes can be prioritized.
Corresponding reference numerals indicate corresponding parts throughout the several views of the drawings.